One of the challenges of language teaching is how to organize the rules
regarding syntax, semantics, or phonology of the language in a meaningful
manner. This not only requires pedagogical skills, but also requires a deep
understanding of that language. While comprehensive materials to develop such
curricula are available in English and some broadly spoken languages, for many
other languages, teachers need to manually create them in response to their
students' needs. This process is challenging because i) it requires that such
experts be accessible and have the necessary resources, and ii) even if there
are such experts, describing all the intricacies of a language is
time-consuming and prone to omission. In this article, we present an automatic
framework that aims to facilitate this process by automatically discovering and
visualizing descriptions of different aspects of grammar. Specifically, we
extract descriptions from a natural text corpus that answer questions about
morphosyntax (learning of word order, agreement, case marking, or word
formation) and semantics (learning of vocabulary) and show illustrative
examples. We apply this method for teaching the Indian languages, Kannada and
Marathi, which, unlike English, do not have well-developed pedagogical
resources and, therefore, are likely to benefit from this exercise. To assess
the perceived utility of the extracted material, we enlist the help of language
educators from schools in North America who teach these languages to perform a
manual evaluation. Overall, teachers find the materials to be interesting as a
reference material for their own lesson preparation or even for learner
evaluation.

本研究介绍了一个自动框架，旨在通过自动发现和可视化语法的不同方面来促进语言教学，其中从自然语料库中提取描述单词排序、一致性、语法标记或词形成及词汇学习的描述，并显示例证。作者在印度语言 Kannada 和 Marathi 上应用此方法，以评估其实用性，并邀请北美的语言教育家对这些语言进行手动评估。教师们认为这些材料有趣，可以用作自己的课程准备或学习者评估的参考资料。

自动提取语法知识对 L2 语言学习的教师感知

Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning

Coordinate compounds (CCs) and elaborate expressions (EEs) are coordinate
constructions common in languages of East and Southeast Asia. Mortensen (2006)
claims that (1) the linear ordering of EEs and CCs in Hmong, Lahu, and Chinese
can be predicted via phonological hierarchies and (2) these phonological
hierarchies lack a clear phonetic rationale. These claims are significant
because morphosyntax has often been seen as in a feed-forward relationship with
phonology, and phonological generalizations have often been assumed to be
phonetically "natural". We investigate whether the ordering of CCs and EEs can
be learned empirically and whether computational models (classifiers and
sequence labeling models) learn unnatural hierarchies similar to those posited
by Mortensen (2006). We find that decision trees and SVMs learn to predict the
order of CCs/EEs on the basis of phonology, with DTs learning hierarchies
strikingly similar to those proposed by Mortensen. However, we also find that a
neural sequence labeling model is able to learn the ordering of elaborate
expressions in Hmong very effectively without using any phonological
information. We argue that EE ordering can be learned through two independent
routes: phonology and lexical distribution, presenting a more nuanced picture
than previous work. [ISO 639-3:hmn, lhu, cmn]

通过决策树和支持向量机，我们发现 coordinate compounds 和 elaborate expressions 的排序可以通过 phonology 预测，但理解 elaborate expressions 的顺序可以通过使用两种独立路线（phonology 和 lexical distribution）学习。

学习苗、拉祜、和汉语中坐标化合物和精细表达的顺序

Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinese

The success of pre-trained contextualized representations has prompted
researchers to analyze them for the presence of linguistic information. Indeed,
it is natural to assume that these pre-trained representations do encode some
level of linguistic knowledge as they have brought about large empirical
improvements on a wide variety of NLP tasks, which suggests they are learning
true linguistic generalization. In this work, we focus on intrinsic probing, an
analysis technique where the goal is not only to identify whether a
representation encodes a linguistic attribute but also to pinpoint where this
attribute is encoded. We propose a novel latent-variable formulation for
constructing intrinsic probes and derive a tractable variational approximation
to the log-likelihood. Our results show that our model is versatile and yields
tighter mutual information estimates than two intrinsic probes previously
proposed in the literature. Finally, we find empirical evidence that
pre-trained representations develop a cross-lingually entangled notion of
morphosyntax.

本文提出了一种新的潜变量公式用于构建内在探测器以确定语言属性所在位置，并提出一个可行的变分逼近方法，用于求解对数似然函数计算，结果表明这个模型能够获得更好的内部探测精度，并且在跨语言的形态句法方面表现良好。